Emerging applications such as in health care, social networks, smart infrastructure, surveillance, and monitoring (including embedded-sensor networks, Business Intelligence data analytics, and computer vision) generate and consume massive data volumes. The consumption of these massive data volumes often includes application of statistical inference techniques, particularly machine learning algorithms, to extract informative patterns of interest. In such systems, energy consumption in memory subsystems tends to dominate system energy-efficiency. In addition, system through-put is often limited by the bandwidth between memory and logic in the applications.
Memory-intensive applications, such as pattern recognition, work most efficiently when higher bandwidth between memory and logic is available since there may only be a few logic operations per memory fetch. To reduce power consumption and increase throughput in memory-intensive applications, 3D integrated circuits, embedded memory (e.g., eDRAM), processor-in-memory (PIM) architectures, associative memories, and low-power memory design are being explored.